A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoen...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 15428 - 18
Hauptverfasser:	Xiong, Qiongbing, Wu, Xuecheng, Yu, Cizhen, Hosseinzadeh, Hasan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 02.05.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/4077 Accuracy Boosted Big-Bang crunch theory Classification Datasets Deep learning Humanities and Social Sciences Land use Land use classification Metaheuristic multidisciplinary Neural networks Optimization techniques Remote sensing Science Science (multidisciplinary) Stacked autoencoder VGG-19 Deep learning Stacked autoencoder Boosted Big-Bang crunch theory Land-use classification Metaheuristic VGG-19
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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